For each dataset, we use the same procedure as [1]} for train-val-test splits. It should be noted that, previous methods such as, [2]}, [3]}, [4]} usually pre-train their models on large (\(\approx \) 1M samples) synthetic manipulation datasets and then fine-tune the models on the datasets mentioned above to report the final result. However, in this paper, to evaluate solely the model's performance, we do not create a synthetic manipulation dataset to pretrain our model. Interestingly, without taking a resort to any large synthetic manipulation dataset, our model outperforms the baseline models.
